{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Calcu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-1dc0c88e3855>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mg1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#define a variable v in g2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'shape'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "#define a variable v in g1\n",
    "g1=tf.Graph()\n",
    "with g1.as_default():\n",
    "    v=tf.get_variable(\"v\",initializer=tf.zeros_initializer(shape=[1]))\n",
    "\n",
    "#define a variable v in g2\n",
    "g2=tf.Graph()\n",
    "with g2.as_default():\n",
    "    v=tf.get_variable(\"v\",initializer=tf.ones_initializer(shape=[1]))\n",
    "    \n",
    "#get v in g1\n",
    "with tf.Session(graph=g1) as sess:\n",
    "    tf.initializer_all_variables().run()\n",
    "    with tf.variable_scope(\"\",reuse=True):\n",
    "        print(sess.run(tf.get_variable(\"v\")))\n",
    "        \n",
    "#get v in g2\n",
    "with tf.Session(graph=g2) as sess:\n",
    "    tf.initializer_all_variables().run()\n",
    "    with tf.variable_scope(\"\",reuse=True):\n",
    "        print(sess.run(tf.get_variable(\"v\")))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"add:0\", shape=(2,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "a=tf.constant([1.0,2.0],name=\"a\")\n",
    "b=tf.constant([2.0,3.0],name=\"b\")\n",
    "\n",
    "result=tf.add(a,b,name=\"add\")\n",
    "print(result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create a session\n",
    "sess=tf.Session()\n",
    "\n",
    "sess.run()\n",
    "sess.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creat a sess and use python to assis it\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(..)\n",
    "#this time we donot need use\"sess.close()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  5.]\n"
     ]
    }
   ],
   "source": [
    "sess=tf.Session()\n",
    "\n",
    "with sess.as_default():\n",
    "    print(result.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  5.]\n",
      "[ 3.  5.]\n"
     ]
    }
   ],
   "source": [
    "sess=tf.Session()\n",
    "print(sess.run(result))\n",
    "print(result.eval(session=sess))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)\n",
    "\n",
    "sess1=tf.InteractiveSession(config=config)\n",
    "sess2=tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.95757794]]\n"
     ]
    }
   ],
   "source": [
    "#define a net just forward\n",
    "import tensorflow as tf\n",
    "\n",
    "w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))\n",
    "w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))\n",
    "\n",
    "x=tf.constant([[0.7,0.9]])\n",
    "\n",
    "a=tf.matmul(x,w1)\n",
    "y=tf.matmul(a,w2)\n",
    "\n",
    "sess=tf.Session()\n",
    "\n",
    "sess.run(w1.initializer)\n",
    "sess.run(w2.initializer)\n",
    "\n",
    "print(sess.run(y))\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#another way to initialize all variable one time\n",
    "init_op=tf.initializer_all_variables()\n",
    "sess.run(init_op)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.05991599]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "w1=tf.Variable(tf.random_normal([2,3],stddev=1))\n",
    "w2=tf.Variable(tf.random_normal([3,1],stddev=1))\n",
    "\n",
    "x=tf.placeholder(tf.float32,shape=(1,2),name=\"input\")\n",
    "a=tf.matmul(x,w1)\n",
    "y=tf.matmul(a,w2)\n",
    "\n",
    "sess=tf.Session()\n",
    "init_op=tf.global_variables_initializer()\n",
    "#init_op=tf.initialize_all_variables()\n",
    "sess.run(init_op)\n",
    "\n",
    "#print(sess.run(y))\n",
    "\n",
    "print(sess.run(y,feed_dict={x:[[0.7,0.9]]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.81131822  1.48459876  0.06532937]\n",
      " [-2.4427042   0.0992484   0.59122431]]\n",
      "[[-0.81131822]\n",
      " [ 1.48459876]\n",
      " [ 0.06532937]]\n",
      "After 0 training step(s), cross entropy on all data is 0.0674925\n",
      "After 1000 training step(s), cross entropy on all data is 0.0163385\n",
      "After 2000 training step(s), cross entropy on all data is 0.00907547\n",
      "After 3000 training step(s), cross entropy on all data is 0.00714436\n",
      "After 4000 training step(s), cross entropy on all data is 0.00578471\n",
      "\n",
      "\n",
      "[[-1.96182752  2.58235407  1.68203771]\n",
      " [-3.46817183  1.06982315  2.11788988]]\n",
      "[[-1.82471502]\n",
      " [ 2.68546653]\n",
      " [ 1.41819501]]\n"
     ]
    }
   ],
   "source": [
    "#define an example to solve two classes\n",
    "import tensorflow as tf\n",
    "\n",
    "from numpy.random import RandomState\n",
    "\n",
    "batch_size=8\n",
    "\n",
    "#define parameter\n",
    "w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))\n",
    "w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))\n",
    "\n",
    "#\n",
    "x=tf.placeholder(tf.float32,shape=(None,2),name='x-input')\n",
    "y_=tf.placeholder(tf.float32,shape=(None,1),name='y-input')\n",
    "\n",
    "#define forward net\n",
    "a=tf.matmul(x,w1)\n",
    "y=tf.matmul(a,w2)\n",
    "\n",
    "#define loss function and  banckword \n",
    "cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))\n",
    "train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy)\n",
    "\n",
    "#generate a set\n",
    "rdm=RandomState(1)\n",
    "dataset_size=128\n",
    "X=rdm.rand(dataset_size,2)\n",
    "Y=[[int(x1+x2<1)]for(x1,x2) in X]\n",
    "\n",
    "#create a session\n",
    "with tf.Session() as sess:\n",
    "    #init_op=tf.initialize_all_variables()\n",
    "    init_op=tf.global_variables_initializer()\n",
    "    sess.run(init_op)\n",
    "    print(sess.run(w1))\n",
    "    print(sess.run(w2))\n",
    "    \n",
    "    STEPS=5000\n",
    "    for i in range(STEPS):\n",
    "        start=(i*batch_size)%dataset_size\n",
    "        end=min(start+batch_size,dataset_size)\n",
    "    \n",
    "        sess.run(train_step,feed_dict={x:X[start:end], y_:Y[start:end]})\n",
    "    \n",
    "        if i%1000==0:\n",
    "            total_cross_entropy=sess.run(cross_entropy,feed_dict={x:X,y_:Y})\n",
    "            print(\"After %d training step(s), cross entropy on all data is %g\"%(i,total_cross_entropy))\n",
    "    print('\\n')\n",
    "    print(sess.run(w1))\n",
    "    print(sess.run(w2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define activate function\n",
    "a=tf.nn.relu(tf.matmul(x,w1)+biases1)\n",
    "y=tf.nn.relu(tf.matmul(a,w2)+biases2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define cross-entropy\n",
    "cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v=tf.constant([[1.0,2.0,3.0],[4.0,5.0,6.0]])\n",
    "print(tf.clip_by_value(v,2.5,4.5).eval())\n",
    "#expected output is:[[2.5,2.5,3.][4,4.5,4.5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"mul_11:0\", shape=(2, 2), dtype=float32)\n",
      "Tensor(\"MatMul_14:0\", shape=(2, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "v1=tf.constant([[1.0,2.0],[3.0,4.0]])\n",
    "v2=tf.constant([[5.0,6.0],[7.0,8.0]])\n",
    "\n",
    "print(v1*v2)\n",
    "\n",
    "print(tf.matmul(v1,v2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v=tf.constant([[1.0,2.0,3.0],[4.0,5.0,6.0]])\n",
    "print(tf.reduce_mean(v).eval())\n",
    "#expected out is 3.5\n",
    "\n",
    "\n",
    "\n",
    "#multil\n",
    "cross_entropy=tf.nn.softmax_cross_entropy_with_logits(y,y_)\n",
    "\n",
    "#\n",
    "mse=tf.reduce_mean(tf.square(y_-y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define a loss function\n",
    "loss=tf.reduce_sum(tf.where(tf.greater(v1,v2),(v1-v2)*a,(v2-v1)*b))\n",
    "\n",
    "#when the first is true, select two,otherwise select three\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True]\n",
      "[ 4.  3.  3.  4.]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "v1=tf.constant([1.0,2.0,3.0,4.0])\n",
    "v2=tf.constant([4.0,3.0,2.0,1.0])\n",
    "\n",
    "sess=tf.InteractiveSession()\n",
    "print(tf.greater(v1,v2).eval())\n",
    "\n",
    "print(tf.where(tf.greater(v1,v2),v1,v2).eval())\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.81031823]\n",
      " [ 1.4855988 ]]\n",
      "[[-0.8093366]\n",
      " [ 1.4865911]]\n",
      "[[-0.80838311]\n",
      " [ 1.48758912]]\n",
      "[[-0.80742168]\n",
      " [ 1.48854196]]\n",
      "[[-0.80644417]\n",
      " [ 1.4894979 ]]\n",
      "[[-0.80545688]\n",
      " [ 1.49047196]]\n",
      "[[-0.80446237]\n",
      " [ 1.49145281]]\n",
      "[[-0.8034634 ]\n",
      " [ 1.49243844]]\n",
      "[[-0.80245554]\n",
      " [ 1.49342489]]\n",
      "[[-0.80146426]\n",
      " [ 1.49441588]]\n",
      "[[-0.80048096]\n",
      " [ 1.49542165]]\n",
      "[[-0.79950386]\n",
      " [ 1.4964081 ]]\n",
      "[[-0.79850495]\n",
      " [ 1.49741769]]\n",
      "[[-0.79752892]\n",
      " [ 1.49840248]]\n",
      "[[-0.79654419]\n",
      " [ 1.4994123 ]]\n",
      "[[-0.79556257]\n",
      " [ 1.50043571]]\n",
      "[[-0.79462433]\n",
      " [ 1.50143182]]\n",
      "[[-0.79369748]\n",
      " [ 1.50242686]]\n",
      "[[-0.79274493]\n",
      " [ 1.50342429]]\n",
      "[[-0.79180086]\n",
      " [ 1.50438201]]\n",
      "[[-0.79083252]\n",
      " [ 1.50532436]]\n",
      "[[-0.78985554]\n",
      " [ 1.50629151]]\n",
      "[[-0.78887033]\n",
      " [ 1.50725818]]\n",
      "[[-0.78787929]\n",
      " [ 1.50822437]]\n",
      "[[-0.78687394]\n",
      " [ 1.50918674]]\n",
      "[[-0.78588563]\n",
      " [ 1.51015079]]\n",
      "[[-0.78490686]\n",
      " [ 1.51113391]]\n",
      "[[-0.7839362 ]\n",
      " [ 1.51209509]]\n",
      "[[-0.78293514]\n",
      " [ 1.51309192]]\n",
      "[[-0.7819587 ]\n",
      " [ 1.51406181]]\n",
      "[[-0.78097296]\n",
      " [ 1.51506674]]\n",
      "[[-0.77999133]\n",
      " [ 1.51609015]]\n",
      "[[-0.77905494]\n",
      " [ 1.51708424]]\n",
      "[[-0.77813309]\n",
      " [ 1.51807654]]\n",
      "[[-0.77718365]\n",
      " [ 1.51907122]]\n",
      "[[-0.77624506]\n",
      " [ 1.5200237 ]]\n",
      "[[-0.77527946]\n",
      " [ 1.52095795]]\n",
      "[[-0.77430487]\n",
      " [ 1.52191913]]\n",
      "[[-0.77332139]\n",
      " [ 1.52287865]]\n",
      "[[-0.77233142]\n",
      " [ 1.52383661]]\n",
      "[[-0.77132487]\n",
      " [ 1.52478945]]\n",
      "[[-0.77033693]\n",
      " [ 1.52574313]]\n",
      "[[-0.76935965]\n",
      " [ 1.52671778]]\n",
      "[[-0.76839161]\n",
      " [ 1.5276686 ]]\n",
      "[[-0.76738876]\n",
      " [ 1.52865994]]\n",
      "[[-0.7664125 ]\n",
      " [ 1.52962244]]\n",
      "[[-0.76542616]\n",
      " [ 1.53062487]]\n",
      "[[-0.76444453]\n",
      " [ 1.53164828]]\n",
      "[[-0.76351053]\n",
      " [ 1.53264046]]\n",
      "[[-0.76259267]\n",
      " [ 1.53363061]]\n",
      "[[-0.7616455 ]\n",
      " [ 1.53462315]]\n",
      "[[-0.76071036]\n",
      " [ 1.53557146]]\n",
      "[[-0.75974637]\n",
      " [ 1.5365001 ]]\n",
      "[[-0.75877297]\n",
      " [ 1.53745723]]\n",
      "[[-0.75779021]\n",
      " [ 1.53841221]]\n",
      "[[-0.75680059]\n",
      " [ 1.53936517]]\n",
      "[[-0.75579304]\n",
      " [ 1.54031241]]\n",
      "[[-0.75480515]\n",
      " [ 1.54126012]]\n",
      "[[-0.75382864]\n",
      " [ 1.54223001]]\n",
      "[[-0.75286204]\n",
      " [ 1.54317486]]\n",
      "[[-0.75185806]\n",
      " [ 1.54416323]]\n",
      "[[-0.75088191]\n",
      " [ 1.54512143]]\n",
      "[[-0.74989522]\n",
      " [ 1.54612231]]\n",
      "[[-0.74891359]\n",
      " [ 1.54714572]]\n",
      "[[-0.74798131]\n",
      " [ 1.54813671]]\n",
      "[[-0.74706608]\n",
      " [ 1.54912543]]\n",
      "[[-0.74612039]\n",
      " [ 1.55011666]]\n",
      "[[-0.74518746]\n",
      " [ 1.55106211]]\n",
      "[[-0.74422449]\n",
      " [ 1.55198705]]\n",
      "[[-0.7432518 ]\n",
      " [ 1.55294156]]\n",
      "[[-0.74226946]\n",
      " [ 1.55389369]]\n",
      "[[-0.74128002]\n",
      " [ 1.55484354]]\n",
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      "[[ 1.01951063]\n",
      " [ 1.0429374 ]]\n",
      "[[ 1.01941502]\n",
      " [ 1.04277194]]\n",
      "[[ 1.01937056]\n",
      " [ 1.04280818]]\n",
      "[[ 1.01940894]\n",
      " [ 1.04279339]]\n",
      "[[ 1.01942825]\n",
      " [ 1.04287517]]\n",
      "[[ 1.01941514]\n",
      " [ 1.04288208]]\n",
      "[[ 1.01934695]\n",
      " [ 1.04280889]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from numpy.random import RandomState\n",
    "\n",
    "batch_size=8\n",
    "\n",
    "x=tf.placeholder(tf.float32,shape=(None,2),name='x-input')\n",
    "\n",
    "y_=tf.placeholder(tf.float32,shape=(None,1),name='y-input')\n",
    "\n",
    "#define forward\n",
    "w1=tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))\n",
    "y=tf.matmul(x,w1)\n",
    "\n",
    "loss_less=10\n",
    "loss_more=1\n",
    "\n",
    "loss=tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*loss_more,(y_-y)*loss_less))\n",
    "\n",
    "train_step=tf.train.AdamOptimizer(0.001).minimize(loss)\n",
    "\n",
    "\n",
    "rdm=RandomState(1)\n",
    "dataset_size=128\n",
    "X=rdm.rand(dataset_size,2)\n",
    "\n",
    "\n",
    "Y=[[x1+x2+rdm.rand()/10.0-0.05] for (x1,x2) in X]\n",
    "#train the net\n",
    "with tf.Session() as sess:\n",
    "    init_op=tf.global_variables_initializer()\n",
    "    sess.run(init_op)\n",
    "    STEPS=5000\n",
    "    for i in range(STEPS):\n",
    "        start=(i*batch_size)%dataset_size\n",
    "        end=min(start+batch_size,dataset_size)\n",
    "        \n",
    "        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})\n",
    "        print(sess.run(w1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size=n\n",
    "\n",
    "x=tf.placeholder(tf.float32,shape=(batch_size,2),name='x-input')\n",
    "y_=tf.placeholder(tf.float32,shape=(batch_size,1),name='y-input')\n",
    "\n",
    "#define loss\n",
    "loss=...\n",
    "train_step=tf.train.AdamOptimizer(0.001).minimize(loss)\n",
    "\n",
    "#train the net\n",
    "with tf.Session() as sess:\n",
    "    #parament initialize\n",
    "    ...\n",
    "    for i in range(STEPS):\n",
    "        currnet_X,current_Y=...\n",
    "        sess.run(train_step,feed_dict={x:current_X,y_:current_Y})\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#decay\n",
    "decayed_learning_rate=learning_rate*decay_rate^(global_step/decay_steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one way to solve overfiting\n",
    "import tensorflow as tf\n",
    "\n",
    "#get one layer'e weight\n",
    "def get_weight(shape,lambda):\n",
    "    var=tf.Variable(tf.random_normal(shape),dtype=tf.float32)\n",
    "    tf.add_to_collection('loss',tf.contrib.layer.l2_regularizer(lambda)(var))\n",
    "    return var\n",
    "\n",
    "x=tf.placeholder(tf.float32,shape=(None,2))\n",
    "y_=tf.placeholder(tf.float32,shape=(None,1))\n",
    "batch_size=8\n",
    "\n",
    "#define node in each layer\n",
    "layer_dimension=[2,10,10,10,1]\n",
    "#deine the layers\n",
    "in_dimension=layer_dimension[0]\n",
    "\n",
    "#create 5 layers\n",
    "for i in range(1,n_layers):\n",
    "    out_dimension=layer_dimension[i]\n",
    "    \n",
    "    weight=get_weight([in_dimension,out_dimension],0.001)\n",
    "    bias=tf.Variable(tf.constant(0.1,shape=[out_dimension]))\n",
    "    \n",
    "    #use relu\n",
    "    cur_layer=tf.nn.relu(tf.matmul(cur_layer,weight)+bias)\n",
    "    #update node\n",
    "    in_dimension=layer_dimension[i]\n",
    "    \n",
    "mse_loss=tf.reduce_mean(tf.square(y_-cur_layer))\n",
    "\n",
    "tf.add_to_collection('losses',mse_loss)\n",
    "\n",
    "#return a list\n",
    "\n",
    "loss=tf.add_n(tf.get_collection('losses'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-b966a759e84b>, line 17)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-b966a759e84b>\"\u001b[0;36m, line \u001b[0;32m17\u001b[0m\n\u001b[0;31m    sess.run(tf.assign(v1,5))\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# an example about ExpoentialMovingAverage\n",
    "import tensorflow as tf\n",
    "v1=tf.Variable(0,dtype=tf.float32)\n",
    "step=tf.Variable(0,trainable=False)\n",
    "\n",
    "#define a class\n",
    "ema=tf.train.ExponentialMovingAverage(0.99,step)\n",
    "\n",
    "maintain_average_op=ema.apply([v1])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init_op=tf.global_variables_initializer()\n",
    "    sess.run(init_op)\n",
    "    \n",
    "    print(sess.run([v1,ema.average(v1)])\n",
    "    \n",
    "    sess.run(tf.assign(v1,5))\n",
    "    \n",
    "    sess.run(maintain_average_op)\n",
    "    print(sess.run([v1,ema.average(v1)]))\n",
    "    \n",
    "    sess.run(tf.assign(step,10000))\n",
    "    sess.run(tf.assign(v1,10))\n",
    "    \n",
    "    sess.run(maintain_averages_op)\n",
    "    \n",
    "    print(sess.run([v1,ema.average(v1)]))\n",
    "    \n",
    "    sess.run(maintain_averages_op)\n",
    "    print(sess.run([v1,ema.average(v1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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